TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-365-2018Consistent biases in Antarctic sea ice concentration simulated by climate modelsConsistent biases in Antarctic sea ice concentration simulated by climate modelsRoachLettie A.lettie.roach@niwa.co.nzhttps://orcid.org/0000-0003-4189-3928DeanSamuel M.RenwickJames A.https://orcid.org/0000-0002-9141-2486National Institute of Water and Atmospheric Research, 301 Evans Bay Parade, Greta Point, Wellington 6021, New ZealandSchool of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington 6021, New ZealandLettie A. Roach (lettie.roach@niwa.co.nz)29January20181213653834July201722December201714November201718July2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://tc.copernicus.org/articles/12/365/2018/tc-12-365-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/365/2018/tc-12-365-2018.pdf
The simulation of Antarctic sea ice in global climate models often
does not agree with observations. In this study, we examine the
compactness of sea ice, as well as the regional distribution of sea
ice concentration, in climate models from the latest Coupled Model
Intercomparison Project (CMIP5) and in satellite observations. We
find substantial differences in concentration values between
different sets of satellite observations, particularly at high
concentrations, requiring careful treatment when comparing to
models. As a fraction of total sea ice extent, models simulate too
much loose, low-concentration sea ice cover throughout the year, and
too little compact, high-concentration cover in the summer. In spite
of the differences in physics between models, these tendencies are
broadly consistent across the population of 40 CMIP5 simulations,
a result not previously highlighted. Separating models with and
without an explicit lateral melt term, we find that inclusion of
lateral melt may account for overestimation of low-concentration
cover. Targeted model experiments with a coupled ocean–sea ice
model show that choice of constant floe diameter in the lateral melt
scheme can also impact representation of loose ice. This suggests
that current sea ice thermodynamics contribute to the inadequate
simulation of the low-concentration regime in many models.
Introduction
The cycle of sea ice growth and melt in the Southern Ocean is one
of the largest seasonal signals on Earth. The heterogeneity of the
sea ice cover and distribution of open water areas determine
regional albedo, the reflectivity of the Earth's surface. This in
turn impacts entrainment of irradiative energy into the ocean
mixed layer and the atmospheric energy budget
. Sea ice production, which increases salinity, in
areas of open water strongly impacts the rate of Antarctic Bottom
Water formation , the deepest water mass. Regional
sea ice concentration thus plays an important role in the coupled
climate system.
Coupled climate model output collated by the World Climate
Research Programme (WCRP) under the Coupled Model Intercomparison
Project (CMIP) protocol are a valuable resource for understanding
Earth's climate system. Over 20 groups worldwide have contributed
simulations to the latest project (CMIP5) from their models, many
of which are developed independently and include different
physics. The sea ice components of these models range in
complexity, from single-layer, ocean-advected, limited-rheology
models (e.g. HadCM3; ) to multi-layer,
multiple thickness category models with a non-linear viscous
plastic rheology and explicit melt pond formation (e.g. NorESM;
). Advances in Earth system modelling have
somewhat improved simulation of Arctic sea ice compared to the
previous intercomparison project (CMIP3) , although
this may reflect changes in forcings or
tuning strategy rather than changes in model
physics. Simulation of Antarctic sea ice is not considered to have
improved .
To make assessments like these, most model evaluation studies
quantify agreement between sea ice models and observations using
sea ice extent, which is simply the area of all grid cells with
more than 15 % sea ice concentration. find
a wide range of seasonal cycles and trends in Antarctic sea ice
extent across the CMIP5 ensemble. Compared to observations, they
find that a majority of models underestimate the minimum sea ice
extent in February. evaluate simulated sea ice
volume and thickness as well as sea ice extent, finding that the
CMIP5 multi-model ensemble mean sea ice extent is fairly well
simulated, though worse in the Antarctic than in the Arctic, but
suggest that the sea ice cover is generally too
thin. find that all models overestimate inter-annual
variability of Antarctic sea ice extent, particularly in
winter. They conclude that no CMIP5 model produces Antarctic sea
ice in reasonable agreement with observations over the satellite
era.
Using only sea ice extent means that these model evaluation
studies do not take into account any sub-grid-scale sea ice
information, nor the regional distribution of sea ice. As discussed
by and , model simulations with the
same sea ice extent could have very different sea ice cover
characteristics. instead examines the frequency
distribution of summer Arctic sea ice concentration, finding that
around half the CMIP5 models have a “compact” ice cover (>0.4
of grid cells with more than 90 % sea ice concentration)
and the rest have a “loose” ice cover. present
a similar analysis for the Antarctic, but show only the CMIP5
multi-model mean and do not discuss the results in detail,
focusing instead on the alternative metrics they developed.
In this study we examine model agreement with observations using
various simple metrics that account for sea ice concentration
values and the regional distribution of sea ice. Our aim is to
identify biases in Antarctic sea ice that are common across
multiple models. We then carry out targeted model experiments to
investigate the role of sea ice model thermodynamics in these
biases.
MethodsCMIP5 models
A series of experiments from different global climate models were
carried out for the Coupled Model Intercomparison Project, Phase 5
(CMIP5; ). Output is freely available online from
the Program for Climate Model Diagnosis and Intercomparison. The
historical experiments, which are forced by observed natural and
anthropogenic forcings, end in 2005. To obtain a more contemporary
overview, we also consider the first 9 years of projection
experiments from the midrange mitigation emission scenario
(RCP4.5). Due to the availability of observations (see below), we
conduct analysis using 1992–2014. We select the first ensemble
member for all models that provide monthly sea ice concentration
for both the historical and RCP4.5 experiments, resulting in a set
of 40 models (see Table ).
Observations
Passive microwave radiometers deployed on satellites measure the
brightness temperature of the Earth's surface, and can be used to
infer sea ice concentration. There can be large differences between
satellite observations , as various observational data
sets apply different algorithms to convert passive-microwave
signals into sea ice concentration. As summarized by ,
differences between algorithms are caused by (1) choice of
radiometer channels; (2) tie points, which are the brightness
temperatures used to identify different surfaces; (3) sensitivities
to changes in physical temperature of the surface; and (4) weather
filters, which correct for atmospheric effects falsely indicating
the presence of sea ice.
To account for some of this product uncertainty, we use three
observational data sets: the Bootstrap algorithm , the
NASA Team algorithm , and the ASI algorithm
. We do not consider data sets that merge
different observation methodologies. Bootstrap uses cluster
analysis of brightness temperatures from two channels (19 and
37 GHz vertical polarization in the Antarctic), applies an
ocean mask, and is available from 1979 at a resolution of
25 km. NASA Team uses ratios of brightness temperatures
(which tends to cancel out physical temperature effects) from three
channels (19 GHz in the vertical and horizontal,
37 GHz in the vertical), removes weather contamination
based on certain spectral gradient ratios, and is available from
1979 at a resolution of 25 km. The ASI algorithm uses the
difference in brightness temperatures between horizontal and
vertical polarization at 85 GHz, uses lower-frequency
channels at lower resolution to filter atmospheric effects (which
are more apparent at 85 GHz than lower frequencies), and is
available from 1992 at a resolution of 12 km. We choose to
conduct our analysis over 1992–2014. Bootstrap and NASA Team data
are available as monthly output; ASI-SSMI data are only available as
daily output, so the concentration fields are averaged for each
month.
CMIP5 models used in this study. SIC denotes sea ice concentration.
Short nameCountryResolutionSea ice modelExplicit lateral melt termACCESS1-0Australia1∘×1∘ tripolarCICE4.1As Sect. ACCESS1-3Australia1∘×1∘ tripolarCICE4.1As Sect. bcc-csm1-1China1∘×(1-13)∘ tripolarSISNot included bcc-csm1-1-mChina1∘×(1-13)∘ tripolarSISNot included CanCM4Canada1.875∘×1.875∘ T63 GaussianCanSIM1Unknown (reference N/A)CanESM2Canada1.875∘×1.875∘ T63 GaussianCanSIM1Unknown (reference N/A)CCSM4USA1.11∘× (0.27–0.54)∘ dipolarCICE4As Sect. CESM1-BGCUSA1.11∘× (0.27–0.54)∘ dipolarCICE4As Sect. CESM1-CAM5USA1.11∘× (0.27–0.54)∘ dipolarCICE4As Sect. CMCC-CMItalyORCA-2∘ tripolarLIM2Not included CMCC-CMSItalyORCA-2∘ tripolarLIM2Not included CNRM-CM5FranceORCA-1∘ tripolarGELATO5Thickness-dependent parametrizationCSIRO-Mk3-6-0Australia1.875∘×0.94∘ T63 Gaussianin-houseIncluded, but unclear how it impactsSIC EC-EARTHEUORCA-1∘ tripolarLIM2Not included FGOALS-g2China(1-12)×(1-12)∘ tripolarCSIM5As Sect. GFDL-CM2p1USA1∘×1∘ tripolarSISNot included GFDL-CM3USA1∘×1∘ tripolarSISNot included GFDL-ESM2GUSA1∘×1∘ tripolarSISNot included GFDL-ESM2MUSA1∘×1∘ tripolarSISNot included GISS-E2-HUSA1∘×1∘ tripolarin-houseNot included GISS-E2-H-CCUSA1∘×1∘ tripolarin-houseNot included GISS-E2-RUSA1∘×1.25∘in-houseNot included GISS-E2-R-CCUSA1∘×1.25∘in-houseNot included HadCM3UK1.25∘×1.25∘in-houseNot included HadGEM2-AOSouth Korea1∘×1∘CICE-likeParametrization for SIC <5%HadGEM2-CCUK(1-13)∘× 1∘CICE-likeParametrization for SIC <5%HadGEM2-ESUK(1-13)∘× 1∘CICE-likeParametrization for SIC <5%inmcm4Russia1∘×12∘in-houseEmpirical parametrization IPSL-CM5A-LRFranceORCA-2∘ tripolarLIM2Not included IPSL-CM5A-MRFranceORCA-2∘ tripolarLIM2Not included IPSL-CM5B-LRFranceORCA-2∘ tripolarLIM2Not included MIROC4hJapan0.28∘×0.19∘in-houseNot included MIROC5Japan1.4∘× (0.5–1.4)∘in-houseNot included MIROC-ESMJapan1.4∘×1∘in-houseNot included MIROC-ESM-CHEMJapan1.4∘×1∘in-houseNot included MPI-ESM-LRGermany1.5∘×1.5∘in-houseNot included MPI-ESM-MRGermany0.4∘×0.4∘in-houseNot included MRI-CGCM3Japan1∘×0.5∘ tripolarin-houseNot included NorESM1-MNorway1.11∘× (0.25–0.54)∘CICE4.1As Sect. NorESM1-MENorway1.11∘× (0.25–0.54)∘CICE4.1As Sect.
Differences between the three selected data sets are large: in the
Antarctic, the NASA Team algorithm shows the marginal ice zone
(defined as the extent of sea ice with concentration between 15
and 80 %) to extend over 2 million km more than the
Bootstrap algorithm in the winter months . NASA Team
is more sensitive to clouds and wind over open water than the
Bootstrap mode , while the high-frequency ASI
algorithm is also sensitive to such atmospheric effects
. Bootstrap is more sensitive to physical temperature
changes than NASA Team, and may underestimate concentrations at
low temperatures, such as near the Antarctic coast
. For low concentrations, atmospheric effects,
which generally lead to falsely increased sea ice, become
increasingly important . The weather
filters/ocean masks used to correct these differ between the
different algorithms.
Besides structural uncertainty in observational algorithms,
systematic biases common to all three products are possible. Lack
of validation data mean it is difficult to quantify
this, but accuracy is understood to be lower in the presence of
melt ponds or other surface melt effects , which may
act to lower retrieved concentrations; large fractions of thin ice
; and stormy conditions near low concentrations
. Transitions between ice type can cause
differences in emissivity , but because models
do not simulate ice types such as grease ice, this issue should
not impact model–observation comparisons.
In this study, for some of the analysis we consider the three
observational data sets individually. In order to compare the sea
ice concentration distribution from the set of models against
observations, we create an ensemble of the ASI-SSMI, Bootstrap, and
NASA Team observational products. Combining the observational
products in this way does have limitations, as different
algorithms are likely to perform better for certain sea ice
conditions and seasons. However, it is not clear from the
literature where exactly the strengths of the various algorithms
lie, and evaluation of the different algorithms is beyond the
scope of this paper. The difficulty in ranking various
observational algorithms is noted by , due to a lack
of validation data. They recommend constructing an ensemble of
different observational products.
Metrics
Following convention, sea ice extent is defined as the area of all
grid cells with more than 15 % sea ice concentration. Sea
ice area is the sum of the area of all grid cells with more than
15 % sea ice concentration multiplied by the sea ice
concentration in each grid cell.
To account for misplacement of sea ice, we use the integrated
ice-edge error (IIEE) from . The IIEE describes the
area of grid cells where observations and a model disagree on the
presence of sea ice with concentration greater than
15 %. It can be decomposed into the total sea ice extent
difference between model and observations (absolute extent error,
AEE) and the difference in sea ice extent due to misplacement of
sea ice (misplacement extent error, MEE). See for
further details.
Here, we also define an integrated ice area error (IIAE) that
describes the area of sea ice on which models and observations
disagree. The ice area on which models and observations disagree
is likely to be more physically relevant than the area of grid
cells on which models and observations disagree. The IIAE is the
sum of sea ice area overestimated and underestimated,
IIAE=O+U
with
O=∫Amax(cm-co,0)dA
and
U=∫Amax(co-cm,0)dA,
where A is the area of interest, cm is the
simulated sea ice concentration, and co is the
observed sea ice concentration.
The integrated ice errors are useful as they quantify error in
integrated sea ice concentration values as well as quantifying
error caused by sea ice appearing in different grid cells than the
observations. This is in contrast to difference in sea ice area,
which accounts only for error in integrated sea ice concentration
values, and difference in sea ice extent, which accounts only for
error in the area of grid cells that have ice. The integrated ice
errors penalize underestimation and overestimation of sea ice
equally.
In this study we also consider sea ice concentration distributions,
as in and . The sea ice concentration
distribution for each model or observational product is calculated
by binning grid cells according to their concentration at
a 10 % spacing. The distribution is then normalized by the
area of grid cells. We follow the same calculation steps as
. This metric allows us to examine observed and
modelled behaviour in different sea ice concentration regimes. It
does not penalize models whose spatial distribution of sea ice
disagrees with observations, but it does allow us to quantify
disagreement with observations on sea ice concentration values
while accounting for the observational range.
To look for behaviours which are consistent across all CMIP5
models, we compare the population of all models for the years
1992–2014 against the population of all observations for the same
period. Including all models means that the range is large when
models show opposite tendencies; using a multi-model mean would
average out this information. Including all months in each season
for all years during analysis captures sub-seasonal and
inter-annual variability.
To quantify the agreement between two populations, we use the
two-sample Kolmogorov–Smirnov test. This compares the empirical
distribution functions of each sample, and takes into account both
the location and shape of the distributions. In contrast,
a Student's t test would only examine whether the means of the
distributions agree. The p value obtained from the
Kolmogorov–Smirnov test represents the confidence that the two
populations come from the same distribution.
We found that sea ice concentration distributions show some
sensitivity to grid interpolation and therefore calculate sea ice
concentration distributions, as well as sea ice area, on the native
model and observation grids. The integrated ice errors and
differences in sea ice concentration fields between models and
observations must be calculated on the same grid. In these cases,
we follow and interpolate model output and
observational data on to a common grid using the bilinear remapping
function from Climate Data Operators (). For the
CMIP5 integrated ice errors and sea ice concentration differences,
we choose a 1∘×1∘ regular grid, which is
a resolution equal to or higher than 20 of the 40 models and lower
than all observations. We consider it to be an acceptable midpoint
given the large range of model resolutions.
Coupled ocean–sea ice model
To understand the impact of model parametrizations for sea ice
thermodynamics, we carry out perturbed parameter simulations using
a coupled ocean–sea ice model. This consists of the ocean model
NEMO and the sea ice model CICE5.1 forced with the atmospheric
reanalysis JRA-55 , run on a 1∘ tripolar
grid. CICE is a state-of-the-art sea ice model and is used as the
sea ice component for several of the CMIP5 models
(Table ). Below we briefly explain the model's sea
ice thermodynamics; further details may be found in .
CICE describes the evolution of the ice thickness distribution in
five discrete categories. A volume of new sea ice growth is
calculated from the ocean freezing/melting potential
Ffrz/mlt, with new ice added as area in the smallest
thickness category until the open water fraction is closed, after
which it grows existing ice thickness. For sea ice melt, the net
downward heat flux from the ice into the ocean, Fbot
is
Fbot=-ρwcwchu*Tw-Tf,
where ρw and cw are the density and
heat capacity of sea water, ch=0.006 is the heat
transfer coefficient, u* is the friction velocity,
Tw is the sea surface temperature and
Tf is the ocean freezing temperature, following
. The balance of this flux with a conductive flux
through the ice determines basal melt.
A fraction of ice is also melted laterally following
. If floes have a mean caliper diameter L, their
perimeter is p=πL and their horizontal surface area is
s=αL2 (where α≈0.66 accounts for the
non-circularity of floes and was determined empirically by
). It is assumed that melting occurs uniformly at
a rate wlat around the perimeter of each floe, i.e.
dsdt=wlatp.
Therefore the change in diameter is
dLdt=π2αwlat.
For a region containing n floes with only a single diameter L,
with a total horizontal area stot, the total
concentration A is
A=nstots(L)=nstotαL2.
Hence, with stot and n constant in time and letting
the subscript o denote the initial state,
A=AoLLo2.
Differentiating this and inserting dL/dt then gives the change in
concentration
dAdt=AπLαwlat.
CICE uses a uniform lateral melt rate of
wlat=m1Tw-Tfm2,
which was based on , who found a complex boundary
layer adjacent to vertical ice walls melting in saltwater in the
laboratory, with convective motions following different flow
regimes. The region adjacent to the turbulent flow regime showed
the largest lateral melt rate, which could be fitted to the above
relation. The coefficients m1 and m2 are the best fit to data
quoted by , measured in a single static lead in the
Canadian Arctic archipelago over a 3-week period. In order to
apply Eq. (), CICE assumes a single floe diameter of
L=300m throughout the ice pack. This is one of the more
sophisticated schemes for lateral melt in the CMIP5 models; often
it is not included at all (Table ).
The experiments described below, which are performed with the
coupled NEMO-CICE model, begin in 1979 and end in 2014. The years
before 1992 are neglected to allow for model spin-up. Time series
of annual maximum sea ice extent show that this takes around 10
years to stabilize. Model output from the NEMO-CICE experiments is
analysed on its native grid (1∘ tripolar). Comparisons
between NEMO-CICE simulations and observations (integrated ice
errors and sea ice concentration differences) are computed by
interpolating observations on to the same 1∘ tripolar grid
using .
Sea ice area for the months where the maximum (a, b) and
minimum (c, d) of the seasonal cycle occur. Populations include data
from all years from 1992 to 2014 with box plots for (a, c) the three
observational products (ASI-SSMI, Bootstrap, and NASA Team) and all CMIP5
models listed in Table individually, and (b, d) for
the ensemble of observational products and the CMIP5 model ensemble. Boxes
extend from the lower to upper quartile values of the data with a line at the
median. Whiskers show 1.5 times the interquartile range; beyond this data are
considered outliers and plotted as individual points. The text labels in
(b, d) is the p value calculated from a Kolmogorov–Smirnov test,
which represents the confidence that the two populations come from the same
distribution.
Various ice errors for the population of CMIP5 models for all years
from 1992 to 2014. Errors are shown relative to (red) the ASI-SSMI satellite
observations, (grey) the Bootstrap satellite observations, and (light blue)
the NASA Team observations for the months where the maximum and minimum of
the seasonal cycle occur of sea ice area (a) or of sea ice extent
(b, d) occur. The errors shown are the integrated ice area error
(a), the integrated ice extent error (b), the absolute
extent error divided by the integrated extent error (c), and the
misplacement extent error divided by the integrated extent error
(d). Box plots are as in Fig. .
Results
Figure shows sea ice
area at the annual maximum and minimum from models and
observations. Examining observations and models shown individually
(Fig. a and c), we find that the
interquartile range arising from inter-annual fluctuations over
1992–2014 is generally smaller than inter-model differences.
Figure b and d group the models and
observations into two populations for comparison. At the annual
maximum (Fig. b), the interquartile range
from the ensemble of observations for 1992–2014 is contained
within the ensemble of models from the same period, with the
medians of the two populations in good agreement. There is no clear
model tendency compared to observations for the sea ice area
maximum. At the minimum (Fig. d), the
interquartile ranges from models and observations show less overlap
than the maximum, with the median from the model ensemble
significantly lower than the median from the observational
ensemble, suggesting a broadly consistent underestimation of sea
ice area at the annual minimum by the CMIP5 models. This tendency
was also noted by for sea ice extent. There are
outliers, which show an overestimation of sea ice area, notably
CSIRO-Mk-3-6-0 and the CESM models. The Kolmogorov–Smirnov test
quantitatively shows that both the maximum and minimum sea ice area
model–observation comparisons are significantly different, but the
difference between models and observations is larger at the summer
minimum than at the winter maximum (Fig. b
and d).
Sea ice concentrations (above 0.1 %) for the three sets of
observations (a–c) and the CMIP5 models (d–ar) for the
month of each model or observation's sea ice area minimum, averaged over
1992–2014. Models marked with a bold (dashed) bounding box have high-ranked
(low-ranked) integrated ice area errors regardless of observational product
used. Integrated ice area errors consider sea ice concentrations >15% for the sea ice field shown.
The normalized sea ice concentration distribution for all months in
each year from 1992 to 2014 in (a) DJF, (b) MAM,
(c) JJA, and (d) SON from the three sets of satellite
observations. Box plots as in Fig. .
The poorer performance of models at the summer minimum is supported
by the integrated ice area error (Fig. a). The
integrated ice area error has a model median value of around
2 million km2 at the sea ice area minimum and around
5.5 million km2 at the sea ice area maximum, despite a much
larger amplitude in model mean sea ice area values (around
15 million and 1 million km2 respectively). Results are
similar using the integrated ice extent error
(Fig. b), although the use of extent rather
than area reduces the variation between observational
references. At the winter maximum, across the population of CMIP5
models and different years, we find that the absolute extent error
and the misplacement extent error contribute approximately equally
to the total integrated ice extent error
(Fig. c–d). At the summer minimum, the
integrated ice extent errors for the CMIP5 models have a slightly
larger contribution from absolute extent errors than from
misplacement area errors (Fig. c–d).
The large inter-model variability in extent and area at the summer
minimum can be seen in Fig. , where the sea
ice concentration fields show diverse behaviour. Variability
between observational products is smaller than inter-model
differences, but observational differences are visible,
particularly at low concentrations. An objective way to quantify
model–observation disagreement is to use the integrated ice area
error, which describes the area of sea ice on which models and
observations disagree. Due to observational variability, we
calculate this relative to each observational product
individually. The variation in observations means that we cannot
rank the models in an overall order, but we can construct two
groups of well-performing models and of poorly performing models
whose members do not change when using different observational
products. These are marked in Fig. .
We now consider sea ice concentration distributions from
observations and models, which provide a more detailed assessment
than hemisphere-integrated measures. A normalized sea ice
concentration distribution may help isolate the role of the sea ice
component, as models with a constant temperature bias in the
atmosphere or ocean, resulting in a biased sea ice area or extent,
may still simulate the relative fraction of different concentration
regimes successfully.
As shown by , the CMIP5 multi-model mean and the NASA
Team observations have a high fraction of ice below 10 %
sea ice concentration in the summer. We find that the fraction of
0.001–10 % concentration ice varies in the models from
0.005 to 1.0 (when models are essentially ice-free) in the summer
(Fig. ). It consists of up to around a third
of the ice in other seasons for some models. Including these very
low concentrations heavily skews the normalized sea ice
concentration distribution towards low concentrations and it
obscures behaviour at higher concentrations. Our aim is to look for
consistent model behaviour, so to avoid the large variance between
different models and between different observations at very low
concentrations, we only consider sea ice concentrations above
10 %. We present all months grouped by meteorological
season (December–February, DJF; March–May, MAM; June–August, JJA; and September–November, SON). This choice separates the
melt season (September–February) from the freezing season
(March–August), while limiting the number of months included in
each season.
The normalized sea ice concentration distribution for all months in
each year from 1992 to 2014 in (a) DJF, (b) MAM,
(c) JJA, and (d) SON from the three sets of satellite
observations (blue) and the 40 CMIP5 models (green).
Box plots as in Fig. . Annotated text is the p value
calculated from a Kolmogorov–Smirnov test, which represents the confidence
that the two populations come from the same distribution.
We first describe satellite observations using the normalized sea
ice concentration distribution (Fig. ). Here,
individual box plots contain both inter-annual and sub-seasonal
variability, while the differences between box plots reflects
uncertainty arising from different processing of satellite
data. Differences between observational products are largest for
compact ice (90 %+) than other concentrations. In
general, the ASI-SSMI observations show more similar
characteristics to the Bootstrap observations than the NASA Team
observations for most of the year, apart from DJF, where the
opposite is true. This results in a somewhat skewed distribution
when considering an ensemble created from three data sets. We find
that the NASA Team algorithm shows a looser ice cover, with
a significantly lower proportion of cover in the 90 %+
concentration bin, than both the Bootstrap and ASI-SSMI
observations. This result holds when considering an un-normalized
sea ice concentration distribution as well (not shown). The
fraction in the 70–90 % bins is larger to compensate. We
also find that differences between data sets persists throughout
the year. This is in contrast to the Arctic, where the frequency of
compact sea ice cover shown in the Bootstrap and NASA Team data sets
shows largest disagreement in the summer, due to issues with
treatment of melt ponds . In the Antarctic,
observational uncertainty in the frequency of compact sea ice is
largest in winter.
Differences between the sea ice concentration distribution from
models and observations, including inter-annual and sub-seasonal
information (Fig. ), are less distinct than
between observational products themselves. This reflects the large
range in both models and observations due to systematic
uncertainties. The overall decomposition from the CMIP5 models,
with a large fraction of compact ice cover and smaller fractions of
lower concentrations is somewhat in agreement with
observations. Agreement appears poorest in DJF, where the lower to
upper quartile range for 90 %+ sea ice concentration
from models and observations overlap very little. Models strongly
underestimate the fraction of sea ice area with concentration
greater than 90 %, that is, their central ice pack is not
compact enough. They tend to overestimate the fraction in the
80–90 % bin and at lower concentrations to compensate. In
other seasons, there appears to be a slight tendency to
overestimate the fraction of compact (90 %+) ice, with
a reduction in the 70–90 % bins to compensate. The
two-sample Kolmogorov–Smirnov test can be used to quantify the
degree of disagreement between models and observations. The
confidence level that the ensemble of observations and ensemble of
models were drawn from the same population has the smallest values
for the 90–100 and 10–20 % in DJF, the 70–90 %
concentrations in MAM, the 10–30 % concentrations in JJA,
and the 80–90 and 10–20 % concentrations in SON. There
is a tendency for models to overestimate the fraction of
low-concentration (10–20 %) sea ice in all seasons. This
overestimation of <20% sea ice compared to
observations is robust when considering sea ice concentration bins
spaced at 5 % intervals and beginning at 15 %,
the cut-off used universally for sea ice extent (not shown). Unlike
the other CMIP5 model tendencies, the overestimation of
10–20 % ice occurs in every month
(Fig. ), with the CMIP5 model median always
outside the interquartile range of the observations.
As discussed above, this assessment takes into account
observational uncertainty and inter-annual and sub-seasonal
variability. That distinct tendencies arise from a population of 40
models, which contain diverse physics and different sea ice, ocean,
and atmosphere models, is striking. It suggests that there is some
deficiency or missing physical process common to many models.
A plausible explanation could be that models form sea ice that is
too thin in the highest bin, which therefore melts more
easily. Conversely, low-concentration sea ice may be too
thick. However, we found no relation between these concentration
biases and average sea ice thickness for the lowest and highest
concentration bins (not shown). We therefore turn to lateral,
rather than vertical, thermodynamics in the next section.
The 10–20 % bin from the normalized sea ice concentration
distribution for each month, where boxes contain all years from 1992 to 2014
from (blue) the three sets of satellite observations and
(green) the 40 CMIP5 models. Box plots as in
Fig. . Annotated text is the p value calculated from
a Kolmogorov–Smirnov test, which represents the confidence that the two
populations come from the same distribution.
The 10–20 % bin from the normalized sea ice concentration
distribution for each month, where boxes contain all years from 1992 to 2014
from (blue) the three sets of satellite observations,
(purple) CMIP5 models that include an explicit lateral melt term, and
(grey) CMIP5 models that do not (from Table ).
Box plots as in Fig. . Annotated text is the p value
calculated from a Kolmogorov–Smirnov test, which represents the confidence
that the two populations come from the same distribution.
Impact of floe size
We hypothesize that the biases in low-concentration Antarctic sea
ice are partially influenced by lateral floe size. Lateral floe
size impacts sea ice concentration through lateral melt only if
included at all in the CMIP5 models (see
Table ). Separating models with and without an
explicit lateral melt term (Table ), we find
a significant difference between the two groups. Models with
explicit lateral melt show a greatly reduced fraction of
low-concentration ice in from March to July compared to models
without, in good agreement with the observations
(Fig. ). Lateral melt can occur all year at
the ice edge, where low concentrations occur.
Figure demonstrates that lateral melt
significantly impacts the normalized sea ice concentration
distribution during autumn. However, lateral melt as it is
currently included in CMIP5 models still results in a tendency
towards overestimation of low-concentration sea ice in other
months, and some models with an explicit lateral melt term
(including the ocean–sea ice model NEMO-CICE) still simulate too
large a fraction of loose ice.
We therefore proceed by examining whether changes to the lateral
melt scheme may also impact the simulation of sea ice. The current
representation of lateral melt in CMIP5 models is heavily
parametrized (Table ), with the formulation
described in Sect. being the most complex
parametrization available in the CMIP5 models. showed
that a more advanced concentration-dependent lateral melt
parametrization significantly impacted the decomposition of sea ice
melt processes, resulting in reduced sea ice concentrations around
the ice edge in the Arctic. In the Antarctic, heat flux from solar
heating of open water areas has been cited as the major cause of
sea ice decay , with this melting potential available
for both lateral and bottom melt. Recent studies have also
suggested that floe size should also impact sea ice concentration
through processes such as floe–floe collisions and lateral growth
.
As shown in Sect. , in CICE the lateral melt flux is
independent of floe size, while the change in concentration arising
from lateral melt is inversely proportional to a constant floe
diameter, D=300m. In reality, sea ice floes can range in
size across orders of magnitude. Several observational studies
(e.g. ; ) find that the number
distribution of floe sizes per unit area follows a power law with
a negative exponent, suggesting that there can be a large number of
small floes.
While concentration is not a proxy for floe size, in general we may
expect that low-concentration areas will be made up of smaller sea
ice floes than high-concentration areas because they are usually
nearer the ice edge. An area of smaller sea ice floes will
experience more lateral melt than an area with a larger floe size
(Eq. ). We therefore suggest that CMIP5 models using the
lateral melt parametrization simulate too much
low-concentration sea ice because this is made up of floes smaller
than 300 m and so should be subject to more lateral
melt. In areas around the ice edge, which are principally
low-concentration, marginal ice zone processes not included in
CMIP5 models, such as wave fracture and dynamic floe interactions,
may further reduce concentrations. Conversely, in
high-concentration areas, floes are likely to be larger than
300 m and therefore should be subject to less lateral melt
than the parametrization prescribes. This could
explain the underestimation of high-concentration sea ice seen in
Antarctic summer.
In order to test this hypothesis, we perform three experiments
using the coupled ocean–sea ice model (NEMO-CICE) described in
Sect. . The experiments have identical set ups apart
from a variation in L, the fixed floe diameter. We run
experiments using (i) the standard value of L=300m, (ii)
a low value of L=1m, and (iii) a high value of
L=10000m. Our perturbed parameter values are constant
and not realistic, but instead are chosen to investigate and
highlight the impact of extreme changes.
The 10–20 % bin from the normalized sea ice concentration
distribution for each month, where boxes contain all years from 1992 to 2014
from a NEMO-CICE simulation with (blue) the three sets of
satellite observations, (light blue) a floe diameter of
300 m (the standard model), and (orange) a floe diameter of
1 m. Box plots as in Fig. . Annotated text is
the p value calculated from a Kolmogorov–Smirnov test, which represents
the confidence that the two populations come from the same distribution.
Sea ice concentration averaged over DJF 1992–2014 for (a)
the standard model simulation with a floe diameter of 300 m;
(b) a model simulation with a floe diameter of 1 m (small
floes) minus (a); and (c) a model simulation with a floe
diameter of 10 000 m (large floes) minus (a).
Panels (d–f) show simulation minus observed Bootstrap sea ice
concentration, where the latter has been interpolated on to the model grid
for (d) the standard model simulation, (e) the small floes
simulation, and (d) the large floes simulation. In (b–f),
differences are shown only if they are statistically different according to
a Student's t test over 1992–2014 (p<5%). Labels on
(d–f) show the integrated ice extent error, absolute extent error
and misplacement extent error in million km2.
Figure shows the fraction of
10–20 % sea ice concentration from observations, the
standard NEMO-CICE model and the model with reduced floe size. The
standard model has very strong overestimation of low-concentration
ice through December to March compared to observations. Impact of
reduced floe size on the distribution is limited, with the
exception of February, where there is a very strong reduction in the
fraction of 10–20 % concentration ice, bringing it into
better agreement with observations.
The enhanced lateral melt achieved by reducing floe size results in
statistically significant reductions in sea ice concentration
relative to the standard model in DJF
(Fig. b). December, January, and February
stand out from the other months in having particularly high total
lateral melt rates. As expected from Fig. ,
enhanced lateral melt reduces the high bias in concentration near
the outer ice edge compared to Bootstrap observations in DJF
(reduction in blue, Fig. d and e), but
enhances the low bias compared to the Bootstrap observations
elsewhere (increase in red, Fig. d and
e). We use the integrated ice extent error described above to
quantify agreement with the Bootstrap observations. The same
qualitative picture is obtained from all three observational
products. We find that the difference in overall agreement with
observations between the standard model and the small floe
simulation is negligible. The absolute extent error significantly
increases in the small floe simulation, because overall this
simulation melts too much ice compared to observations. The
misplacement extent error, however, is significantly reduced in the
small floe simulation. This is partly because there is less ice to
be misplaced, but also because increased lateral melt improves the
distribution of sea ice around the ice edge, by melting areas where
there is too much ice compared to observations
(Fig. d and e).
Besides lateral melt, a number of other physical processes,
including dynamical ones, may also contribute to an overestimation
of low-concentration ice. find systematic
wind-driven biases in sea ice drift speed and direction at the
exterior of the Antarctic ice pack. Errors in surface winds could
contribute to poor simulation of low-concentration sea
ice. However, we find a very strong overestimation in
low-concentration sea ice in the NEMO-CICE model, which is forced
by a reanalysis atmosphere and so should not have very unrealistic
winds. The dynamical response of sea ice to winds at the edge of
the ice may be poorly represented, as we would expect sea ice
dynamics to be floe-size dependent. Alternative rheologies (such as
a granular rheology, e.g. ) may be better
suited to this domain. Concentrations could also be reduced by
mechanical interactions between floes. However, we cannot test the
impact of such floe-size-dependent processes without access to sea
ice models that include them.
The impact of increased floe size, on the other hand, is much
smaller (Fig. c and f). Differences in sea
ice concentration between the standard model and the large floe
simulation are barely perceptible. Changes in the ice errors
relative to the standard model are of the opposite sign compared to
the small floe simulation, but these changes are unlikely to be
significant. Examining the basal and lateral melt rates, we find
that the hemispheric average DJF 1992–2014 mean lateral melt rate
accounts for only 5 % of the combined basal and lateral
melt rates in the standard model. It accounts for a larger
proportion (17 %) of melt in the Arctic. Decreasing floe
diameter by 2 orders of magnitude increases the lateral melt rate
to 83 % of the combined basal and lateral melt. This
compensation effect of reduced basal melt when lateral melt is
increased was also noted by in the Arctic. On the other
hand, increasing the floe diameter by 2 orders of magnitude
effectively switches off lateral melt (0.2 % of combined
basal and lateral melt). In the latter case, more melting potential
is made available for basal melting, which, because Antarctic sea
ice is so thin, has the same impact on sea ice concentration as
lateral melt. We conclude that there must be alternative reasons
for the consistent underestimation of compact summer ice.
Looking at the regional distribution of DJF (the season where the
bias is apparent in Fig. ) seasonal mean sea ice
concentration averaged over 1992–2014, high-concentration
(90–100 %) ice appears in the observations mean only in
the Weddell Sea (Fig. ). Taking the
difference between the high-concentration ice in each observational
product and the sea ice concentration in the CMIP5 model
simulations shows that very few of the models simulate high enough
concentrations in this area. Figure shows
the difference between the ASI-SSMI observations and the CMIP5
models; differences are slightly enhanced using Bootstrap and less
pronounced when using NASA Team. This demonstrates a consistent
model tendency to underestimate concentrations in the Weddell Sea,
the largest region of multi-year ice in Antarctica. The bias is not
present in other seasons, suggesting it is related to overestimated
melt or break-up processes, including misrepresentation of sea ice
dynamics.
Overestimated melt or break-up could be a result of the sea ice
model or a biased warm atmosphere or ocean. While consideration of
normalized sea ice concentration distribution is intended to remove
overall biases caused by (for example) a warm ocean, in summer the
warm ocean could shift the whole distribution to lower
concentrations. Alternatively, or likely in conjunction with this,
regionally important processes may be being
misrepresented. Evaluating the ORCA2-LIM coupled ocean–sea ice
model, found that overestimation of westerly winds
led to an underestimation of sea ice coverage on the eastern side
of the Antarctic peninsula, in the Weddell Sea. Other CMIP5 models
may simulate high drift speeds due to winds or sea ice rheology,
which found correlated with a faster sea ice
retreat.
Simulation minus observed ASI-SSMI sea ice concentration for DJF
1992–2014 for each CMIP5 model, where only grid cells with observational
mean sea ice concentration is ≥90% are considered. Differences
are only shown if they are statistically different according to a Student's
t test over 1992–2014 (p<5%).
Discussion
In this study, we examine the distribution of sea ice
concentration from both models and observations. Firstly, we show
that observed sea ice concentration values can differ
significantly between three widely used algorithms for satellite
data. This observational uncertainty provides a limit beyond which
we cannot further evaluate model agreement with observations.
Many sea ice model–observation comparisons use only one satellite
data set assumed to represent the true observed state, an approach
which may be sufficient when using sea ice extent, a metric where
the various algorithms broadly agree. However, when using metrics
that go beyond sea ice extent, using for example sea ice area or
sea ice concentration distributions, model evaluation studies
should account for the observational range.
We find that simulation of high-concentration (90 %+)
sea ice in models is in better agreement with the NASA Team
observations than the observational range including the Bootstrap
and ASI-SSMI observations, in agreement with , who
only examined the CMIP5 multi-model mean.
Accounting for the range in three observational products, we find
that models overestimate the extent of low-concentration sea ice
throughout the year, while underestimating the extent of
high-concentration sea ice in summer. This common behaviour across
diverse models with varying physics is a result not previously
highlighted and warrants further attention.
We note that using the observational range as an uncertainty
estimate neglects biases that are common to the three different
satellite observations. As mentioned above, sea ice concentrations
are considered to be most uncertain during melt conditions, for
large fractions of thin ice and at low concentrations during
storms. In the context of the results from the model–observation
comparison for normalized sea ice concentration distributions, we
suggest that the impact of uncertainty of melt conditions is
limited as the high bias in low-concentration ice from CMIP5
models is visible throughout the year. The low bias in
high-concentration ice during the melt season would be
strengthened if observations were underestimating ice
concentrations in this season. Inclusion of both NASA Team and
Bootstrap algorithms, with the former tending to cancel out
physical temperature effects, will sample some of this
uncertainty.
The underestimation of sea ice concentrations in areas of thin ice
(<35cm) may cause a bias at any
concentration in the observed normalized sea ice concentration
distribution from observations, with the possibility of a positive
bias in the very lowest concentrations. Stormy conditions near the
ice edge lead to false sea ice concentrations near the ice edge;
weather filters may accurately remove these, leave them
uncorrected , or erroneously remove real sea
ice. The latter may underestimate low
concentrations. suggest the filter method used in
ASI-SSMI observations may result in a positive bias in the
marginal ice zone, and found that the NASA Team
algorithm overestimates low-concentration ice when compared to
Landsat imagery. Considering all this evidence we suggest that the
magnitude or sign of any systematic biases in satellite radiometer
observations is unclear when comparing with climate models. This
is particularly true for low concentrations. Here the use of
different approaches to weather filters within the different
algorithms may assist in sampling observational
uncertainty. Development of sea ice satellite emulators, which use
climate model output to calculate brightness temperatures
(e.g. ), may help to reduce uncertainty when
comparing models to observations in the future.
Categorizing models according to whether they explicitly represent
lateral melting, which is the only thermodynamic sea ice process
that reduces concentrations in models regardless of sea ice
thickness, we find a strong impact of this process on
low-concentration sea ice. In Sect. we briefly
review typical sea ice model thermodynamics, and in particular the
change in concentration induced by lateral melt rate for a region
containing floes of a single diameter, which follows
. finds that development of ocean eddies
due to lateral density gradients could induce much larger lateral
melt than that suggested from the geometric
model. This would support increasing the lateral melt rate in
models, as we have done artificially here through a reduced
constant floe size. Heat budget analysis and
modelling studies suggest that the major cause
of Antarctic sea ice decay is atmospheric heat input to open
water, which causes bottom and lateral melt. find
that sea ice melt by open water plays a larger role in the
Antarctic than in the Arctic. We further note that the
coefficients in the lateral melt rate used in CICE were measured
in the Arctic only and few, if any, observational
studies exist on the relative importance of bottom and lateral
melt in the Antarctic.
The impacts of enhancing lateral melt via reducing a constant floe
size shown here suggest that this process should not be applied in
the same way throughout the ice pack. While not all models include
such a lateral melt parametrization, the biases at the tails of
the concentration distributions from the CMIP5 models point to
inclusion of model processes that are not suitable for both
high-concentration and low-concentration regimes. A possible
conclusion, therefore, is that physics in sea ice models are not
heterogeneous enough to represent observed sea ice cover. Given
the possible contribution of dynamic processes to model biases in
the sea ice concentration distribution, a full exploration of sea
ice dynamics for all CMIP5 models using the sea ice concentration
budget decomposition of would be
welcome. Including information on the floe size distribution and
floe size dependent processes (e.g. ;
; ) could improve consistency with
observations in the metrics presented here.
CMIP5 data are available
at http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. ASI-SSMI data are
available at
http://icdc.cen.uni-hamburg.de/1/daten/cryosphere/seaiceconcentration-asi-ssmi.html. Bootstrap and NASA Team data are available at
https://nsidc.org/data/g02202/versions/3. NEMO-CICE
model output is available from the corresponding author upon request.
LR and SD designed the analysis and experiments and LR carried
them out. LR prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors wish to thank Erik Behrens and Jonny Williams for assistance
setting up and running the coupled ocean–sea ice model, Stephen Stuart for
assistance with CMIP5 output acquisition, and Cecilia Bitz for helpful
discussions in the early stages of this work. We are grateful to the
reviewers, whose comments significantly improved the manuscript. This
research was funded via Marsden contract VUW-1408. We acknowledge the World
Climate Research Programme's Working Group on Coupled Modelling, which is
responsible for CMIP, and we thank the climate modelling groups (listed in
Table 1 of this paper) for producing and making available their model output.
For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis
and Intercomparison provides coordinating support and led development of
software infrastructure in partnership with the Global Organization for Earth
System Science Portals.
Edited by: Dirk Notz
Reviewed by: Francois Massonnet and one anonymous referee
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